Added various activation function nodes and chained model node.

modules/pytorch_wrapper/pt_apply_function.py

@@ -0,0 +1,48 @@
+from typing import Any, Dict, Tuple, Callable
+import torch
+
+
+class PtApplyFunction:
+    """
+    Applies a function to the input tensor.
+    
+    ### Fields:  
+    - tens: Tensor to which the function be applied.
+    - closure: Differentiable function to apply to the tensor.
+
+    category: PyTorch wrapper - Training
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the expected input types.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required and optional input types.
+        """
+        return {
+            "required": {
+                "tens": ("TENSOR", {}),
+                "closure": ("PTCALLABLE", {})
+            }
+        }
+
+    RETURN_TYPES: Tuple[str, ...] = ("TENSOR",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Training"
+
+    def f(self,
+          tens: torch.Tensor,
+          closure: Callable) -> Tuple[torch.Tensor]:
+        """
+        Applies a function to the input tensor.
+
+        Args:
+            tens (torch.Tensor): Tensor to which the function be applied.
+            closure (Callable): Differentiable function to apply to the tensor.
+
+        Returns:
+            Tuple[nn.Module]: A tuple containing the instantiated model.
+        """
+        return (closure(tens),)